We train a separate LiDAR Gating Network that using knowledge distillation [1]}. This one predicts which part of the LiDAR input is the most relevant for driving. We train the LiDAR Gating Network using the gating mechanism obtained in the previous training step as the teacher network. Specifically, the labels \(g_L\)  for training the new gating mechanism are calculated as \(g_L = \sum _i g^L_i r_i\) , where \(g^L_i\)  are outputs of the reference gating mechanism obtained in the first step of the training procedure and \(r_i=[-1.0,-0.5,0.0,0.5,1.0]\)  are the values of the \(g_L\)  corresponding to the segments of the LiDAR image described before.
